Project Aiur

We envision a world where the right scientific knowledge is available at our fingertips. Where all research is validated and reproducible. Where interdisciplinary connections are the norm. Where unbiased scientific information flows freely. Where research already paid for with our tax money is freely accessible to all. Where massive R&D budgets also benefit contributors to core scientific breakthroughs.

Project Aiur at a glance

Democratizing
Science through blockchain-enabled disintermediation.

There are a number of problems in the world of science today hampering global progress. In a highly lucrative oligopolistic industry with terrible incentive misalignments, a radical change is needed.

The only way to change this is with a grassroots movement – of researchers and scientists, librarians, scientific societies, R&D departments, universities, students, and innovators – coming together.

We need to forge an alternative to powerful existing intermediaries, create new incentive structures, build commonly owned tools to validate all research and build a common Validated Repository of human knowledge.

A combination of blockchain and artificial intelligence provides the technology framework, but as with all research, the scientist herself needs to be in the center.

That is what we are proposing with Project Aiur, and we invite you to join us.

Media Coverage

“Money makes the world go round, scientific publishing is no exception to the rule”

Value growth: the Knowledge Validation Engine

The core of the Aiur economy is a community-owned artificial intelligence-based engine for Knowledge Validation – semi-automation of Peer Review, if you like.

All payments for services from the Knowledge Validation Engine will be done to the Aiur Financial Institution. At the same time, new token issuance to community members will be strictly restricted to value contributions. The Aiur Financial Institution will manage AIUR demand and supply flow, burning excess tokens accumulated via a sustained influx of capital. These mechanics govern the value growth of the community economy.

REVENUE STREAM 1:

Direct querying of the KVE

Universities, research institutes and R&D departments spend $128Bn a year on “digital enablers”, and a medium sized department can save millions yearly with tools like the KVE. These organizations have their own internal tools and processes, and will connect these directly to the Aiur API. They pay AIUR to query the engine.

REVENUE STREAM 2:

Third-party applications

A variety of future applications will rely on the Aiur KVE. This will tap into markets such as patent writing and prior art searches, hedge fund technology predictions, research funding and venture capital. 3rd party tools charge their clients and then pay AIUR to query the engine.

The AIUR Token

Functional token
With clear ’proof-of-human-work’ characteristics in its design, the AIUR token is functional by nature. It’s both the only way to tap into Aiur directly and can be a voucher for significantly discounted prices for products built on top of Aiur, including Iris.ai tools.

No short term investors or hodlers
Far from an instrument suited to short-term financial speculation, AIUR tokens are designed for natural holders. Value growth occurs over time with core usage of, and 3rd party applications on top of, the community owned Knowledge Validation Engine.

Capped token sale
Our token sale will target raising the ETH equivalent of c. € 10,000,000, with a minimum for completion of 60% and a hard cap of 500%. If the minimum is not reached, all ETH will be returned to the original holders.

Community ownership off the bat
75% of the amount raised will belong to the community, and will be released subject to development milestones – to anyone who achieves them, subject to community scrutiny. The remaining 25% will be allocated to Iris.ai for the planning and initial execution of the project.

Removing ourself as the central player
There are two phases in Project Aiur. In ‘Phase 1’ Iris.ai will be holding 50% +1 of the tokens in circulation, and after the transition to ‘Phase 2’ we will renounce all tokens outside of the allowed 2% cap, thus becoming an equal community member.

No founder compensation
Iris.ai’s founders will not receive any direct monetary compensation, in either fiat, cryptocurrency or AIUR tokens. Iris.ai, the initiating commercial entity, sees this as a unique strategic opportunity to impact the industry and the world, and commercially to have a first mover advantage on 3rd party applications.

Ecosystem

Contributors

Earn tokens from their
community contributions

AI Trainers
Build annotated data setsImplemented from the
start

Coders
Build the Aiur Knowledge Validatiton engine

Quality Assurance
Find the glitches in the matrix
And bugs in Aiur

Researchers
Publish their own and review others’ research

Aiur

Oracle observing the outside world

Institution maintaining balance

Constitution of rights and obligations

ETH and AIUR pool

Code repository

Research content repository

Users

Pay tokens for access to the
core technology and content

R&D and institutes
Build custom internal tools on top of Aiur functionality

Software developers
Build commercial and open source tools on top of Aiur

Individual researchers
Use the Aiur engine for validation before traditional publishing

Aiur

Oracle observing the outside world

Institution maintaining balance

Constitution of rights and obligations

ETH and AIUR pool

Code repository

Research content repository

The challenges faced by science

Information overload

Challenge

The amount of scientific knowledge we have as a human species is unprecedented and growing. No human mind can cope with the vast volume of research being generated today. This unmanageable information overload slows down and introduces massive inefficiencies in both academic and corporate research processes, hampering global innovation.

Solution

AI-based tools to assist humans in navigating and connecting the knowledge. Iris.ai’s tools today semi-automate the literature review phase – next we need to do machine hypothesis extraction, hypothesis validation and eventually building new hypotheses.

Access barriers

Challenge

Traditional publisher business models are coming under increased scrutiny. The sustained, abnormally high relationship between economic returns yielded and business risks assumed by these legacy models has faced harsh criticism from scientific researchers, academic institutions, policy-makers and the general public alike.

Solution

A united global grassroots movement pushing for Open Access research and putting join pressure on the large publishing houses to alter their business models and pricing schemes. The OA movement has made great progress until now – and it’s time to shift gears.

Poor reproducibility

Challenge

Substandard reproducibility of published research studies adds to pain points suffered by students, researchers and R&D departments across sectors. And when considered in combination with other problems here, reproducibility deficits make it fundamentally hard to build new knowledge on top of old results.

Solution

A combination of 1) a Knowledge Validation Engine: validating every aspect of a research paper up against all other knowledge. This can immediately uncover underlying false assumptions, circular dependencies, conflicting results and other reasons why a paper might not be reproducible before substantial time is spent on it, and 2) increased public scrutiny incentivizing more thorough diligence.

Built-in biases

Challenge

Existing tools focused on scientific search have been built with a common keyword and citation-based architecture that incorporates serious issues with learning-over-time and the identification and address of biases including negligence of under-cited research.

Solution

A long-term process, with users gradually realizing that there are great results de facto not visible through existing search engines. Additionally, Aiur will provide an invaluable control set to test the effectiveness of the citation system and help newly written articles have a more comprehensive and easy to build citation list.

Misaligned incentives

Challenge

Research professionals are currently forced to deliver, publish and review on tight deadlines, with little to no accountability and reward for authors and reviewers, creating perverse incentives towards exaggerating facts and omitting assumptions and constraints.

Solution

Authors will get tokens for embracing increased openness standards, including for publishing of failed results. Aiur will also open up the peer review process, and it will facilitate the generation of specialized datasets.

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